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Inferring causal molecular networks: empirical assessment through a community-based effort.

  • Hill SM MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
  • Heiser LM Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
  • Cokelaer T European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
  • Unger M Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
  • Nesser NK Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
  • Carlin DE Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Zhang Y Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
  • Sokolov A Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Paull EO Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Wong CK Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Graim K Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Bivol A Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Wang H Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
  • Zhu F Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
  • Afsari B Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
  • Danilova LV Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
  • Favorov AV Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
  • Lee WS Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
  • Taylor D Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, North Carolina, USA.
  • Hu CW Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Long BL Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Noren DP Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Bisberg AJ Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Mills GB Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
  • Gray JW Sage Bionetworks, Seattle, Washington, USA.
  • Kellen M Sage Bionetworks, Seattle, Washington, USA.
  • Norman T Sage Bionetworks, Seattle, Washington, USA.
  • Friend S Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Qutub AA Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
  • Fertig EJ Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
  • Guan Y Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
  • Song M Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
  • Stuart JM Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
  • Spellman PT Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
  • Koeppl H IBM Translational Systems Biology and Nanobiotechnology, Yorktown Heights, New York, USA.
  • Stolovitzky G European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
  • Saez-Rodriguez J MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
  • Mukherjee S
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  • 2016-02-23
Published in:
  • Nature methods. - 2016
Algorithms
Causality
Computational Biology
Computer Simulation
Gene Expression Profiling
Gene Regulatory Networks
Humans
Models, Biological
Neoplasms
Protein Interaction Mapping
Signal Transduction
Software
Systems Biology
Tumor Cells, Cultured
English It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
Language
  • English
Open access status
hybrid
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Persistent URL
https://sonar.ch/global/documents/286459
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